What is Predictive Analytics?
“Get science on your side”
There is a science in Predikkta. A science that is reliable, transparent, and forms the fundamental core of Predikkta’s business. The science of Predictive Analytics, also known as Discrete Choice Modelling.
But what is this mystery science?
Predictive Analytics was developed by Nobel Prize winner Daniel McFadden to model the decision process of an individual or segment in a specific context.
Predictive Analytics describes, explains, and predicts choices between two or more discrete alternatives, such as clicking or not clicking on a button, or choosing between modes of transport. It is also different from most other forms of data analytics, which tend to examine “how much” something has changed (and allow people to draw conclusions from the changes). Instead, the focus of Predictive Analytics creates data that allows for the monitoring of real behaviour, and helps determine which option is best.
Example: Australian Winter Festival
Let's first better understand 'how' Predictive Analytics works, with an example of the Australian Winter Festival.
With over 100,000 people attending Winter Festivals in Sydney, Melbourne and Brisbane, receiving international coverage and dominating the niche of winter events in Australia. The Australian Winter Festival is an unqualified success; but how do they do it?
Try and think of all those variables and decisions that were made in running an event like a Winter Festival – and remember that these sort of variables hadn’t occurred before in Australia. Would you have made the same decisions in running the event? There were many alternate Winter Festivals: there’s one where the rink was too small; another where the music was wrong; and another where the food too expensive (or too cheap). There are millions of possible Winter Festivals options, and many are doomed to fail.
Predictive Analytics let the organizers explore those millions of ‘what-ifs’ and predict what would work, so they could plan their event with confidence. Predictive Analytics gave the answer of 'which one' when they needed a particular type of Winter Festival to succeed.
Most importantly; however, is that insights from the predictive model helped make the event both a commercial and cultural success. Learn by foresight, instead of learning via trial and error. Because most of us don’t have the time nor resources to engage in large hit and miss experiments.
About Predictive Analytics
Predictive modelling can be used to predict the outcome in any situation where human choice is involved. Predictive behaviour modelling goes beyond passive customer analytics, by allowing marketers and retention experts to make decisions based on expected future results.
At Predikkta, we have patented our Predictive Analytics algorithm and created a web-based platform to evaluate organic search and paid search results. We are one-of-a-kind!
The SERP Optimizer and the AdWords Optimizer tools allow you to test the effects of altering phrases or even subtle word changes. These tools test across Google in huge numbers of possible combinations, something which is not possible with traditional research methods like A/B testing, where you are limited to testing two options at one time.
Using predictive modelling, you can run 'what-if' experiments. Experiments that can test the effectiveness of the future impact of strategic options. Experiments that will support better decision-making. The web-based platform created by Predikkta lets you experiment with new and alternative search results in a simulated environment.
Subtle word changes in your search result, or focusing on different Call to Actions can have a significant impact on the success of an online campaign. Also, testing in a simulated environment provides the freedom and flexibility to test whatever you want to test. So if you wish, you can test radical changes without impacting your business, buyers, or believability.
Read more about the research process
Predictive Analytics in Practise
Predictive Analytics is already a tool that is used by major multinationals that have the resources to use this costly research method. Predikkta makes Predictive Analytics available to everyone on an intuitive web-based platform. Some examples of where Predictive Analytics are used include:
- Financial Institutions – to model customer valuation of price variables such as interest rates, fee and rewards.
- Government - from the deregulation of electricity in Australia, to determining commuter uptake on Bay Area Rapid Transit system in San Francisco, government utilization of Predictive Analytics has been extensive.
- Advertising Agencies - for concept testing, new product development, and brand equity tracking.
- Health Care Providers - to understand how people value aspects of personal health, as well as health care options for insurance packages and policy decisions.
- Fast Food Chains - to explore the myriad of combinations of fast food ingredients, drinks, special offers, price, promotional messages, and delivery to develop optimal bundled orders.
- Supermarket Products - to explore the billions of possible pricing and packaging configurations of new fast-moving consumer goods product variants.
But no more. The time has come for Predictive Analytics to no longer be in the hands of a select few. The Predikkta Optimizer Tool is the change we all need. It is a tool that makes the science of Predictive Analytics accessible for everyone; meaning that even a local plumber’s campaign can use the same technology that Coca Cola’s campaigns use.